Mature oil fields, which have undergone extensive production and reservoir depletion, present significant challenges in maintaining economic oil recovery. Traditional reservoir characterization approaches often struggle to capture the dynamic complexities of these fields, particularly when heterogeneity, fluid movement, and evolving production conditions are not adequately modeled. This review explores the design of advanced reservoir characterization models that leverage petrophysical data analytics to predict oil flow behavior and optimize field redevelopment strategies. By integrating well log interpretation, core analysis, production history, and seismic-derived petrophysical properties, predictive models can offer improved accuracy in estimating permeability, porosity, saturation, and fluid contact dynamics. The study examines various analytical and machine learning frameworks for interpreting large-scale petrophysical datasets, emphasizing how data-driven methods can complement conventional geological and geophysical modeling. Key topics include the role of multi-scale data integration, uncertainty quantification, and model calibration in mature field conditions. Case studies from diverse basin settings are analyzed to demonstrate practical applications in well placement optimization, enhanced oil recovery (EOR) design, and reservoir simulation updates. The review also addresses challenges such as data quality, model interpretability, and computational scalability. The findings highlight that an integrated petrophysical analytics workflow enables more accurate predictive oil flow modeling, prolonging production life, reducing redevelopment risks, and improving decision-making for late-life field management.